GEO series

Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning

GSE256142 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/09/25 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 18 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 18 samples: 7 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper: Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learningiScience · 2025

Summary (written by the submitters)

Dravet Syndrome (DS) is a developmental epileptic encephalopathy (DEE) driven by pathogenic variants in SCN1A gene. Brain organoids (BO) have emerged as reliable models for neurodevelopmental genetic disorders, reproducing human brain developmental milestones and rising as a promising drug testing tool. Here, we determined the underlaying molecular DS pathophysiology affecting neuronal connectivity, revealing an early onset excitatory-inhibitory imbalance in maturing DS organoids circuitry.

Read the full summary ↓

However, neuronal circuitry modeling in BO remains hampered by the notorious inter- and intra-organoid variability. Thus, leveraging deep learning (DL), we developed ImPheNet, a predictive tool grounded in BO live imaging datasets, to overcome the limitations of the intrinsic BO variability. ImPheNet accurately classified healthy and DS phenotypes at early onset stages, revealing differences between genotypes and upon antiseizure drug exposure. Altogether, our DL-predictive live imaging strategy, ImPheNet, emerges as a powerful tool to accelerate DEE research and advancing towards treatment discovery in a time- and cost-efficient manner.

Published in

Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning

Turpin-Moreno I, Modrego A, Martí-Sarrias A et al. · iScience 2025 · PMID 41323276 · doi:10.1016/j.isci.2025.113831

This dataset

Open GSE256142 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE256142_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1078319 and SRA study SRP490635. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41323276) ↗

Study design

7 conditions, mostly in triplicate

DS_iPSC_d90_n ×3 CTL_iPSC_d90_n ×3 DS_iPSC_d35_n ×3 CTL_iPSC_d35_n ×3 CTL_iPSC_d21_n ×2 DS_iPSC_d90_VPA_n ×2 CTL_iPSC_d90_VPA_n ×2

Supports a between-group comparison across 18 samples.

How we read this: 7 replicated groups read from 18 sample titles; they account for 18 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM8086387 DS_iPSC_d90_n_3 DS_iPSC_d90_n
GSM8086388 CTL_iPSC_d90_n_3 CTL_iPSC_d90_n
GSM8086389 DS_iPSC_d35_n_3 DS_iPSC_d35_n
GSM8086390 CTL_iPSC_d35_n_3 CTL_iPSC_d35_n
GSM8086391 CTL_iPSC_d21_n_1 CTL_iPSC_d21_n
GSM8086392 CTL_iPSC_d21_n_2 CTL_iPSC_d21_n
GSM8086393 DS_iPSC_d35_n_1 DS_iPSC_d35_n
GSM8086394 DS_iPSC_d35_n_2 DS_iPSC_d35_n
GSM8086395 DS_iPSC_d90_n_1 DS_iPSC_d90_n
GSM8086396 DS_iPSC_d90_n_2 DS_iPSC_d90_n
GSM8086397 DS_iPSC_d90_VPA_n_1 DS_iPSC_d90_VPA_n
GSM8086398 DS_iPSC_d90_VPA_n_2 DS_iPSC_d90_VPA_n
GSM8086399 CTL_iPSC_d35_n_1 CTL_iPSC_d35_n
GSM8086400 CTL_iPSC_d35_n_2 CTL_iPSC_d35_n
GSM8086401 CTL_iPSC_d90_n_1 CTL_iPSC_d90_n
GSM8086402 CTL_iPSC_d90_n_2 CTL_iPSC_d90_n
GSM8086403 CTL_iPSC_d90_VPA_n_1 CTL_iPSC_d90_VPA_n
GSM8086404 CTL_iPSC_d90_VPA_n_2 CTL_iPSC_d90_VPA_n

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.